# Copyright 2021 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
"""Dice"""
import numpy as np
from mindspore._checkparam import Validator as validator
from .metric import Metric, rearrange_inputs
[docs]class Dice(Metric):
r"""
The Dice coefficient is a set similarity metric. It is used to calculate the similarity between two samples. The
value of the Dice coefficient is 1 when the segmentation result is the best and is 0 when the segmentation result
is the worst. The Dice coefficient indicates the ratio of the area between two objects to the total area.
The function is shown as follows:
.. math::
dice = \frac{2 * (pred \bigcap true)}{pred \bigcup true}
Args:
smooth (float): A term added to the denominator to improve numerical stability. Should be greater than 0.
Default: 1e-5.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> import numpy as np
>>> from mindspore import nn, Tensor
>>>
>>> x = Tensor(np.array([[0.2, 0.5], [0.3, 0.1], [0.9, 0.6]]))
>>> y = Tensor(np.array([[0, 1], [1, 0], [0, 1]]))
>>> metric = nn.Dice(smooth=1e-5)
>>> metric.clear()
>>> metric.update(x, y)
>>> dice = metric.eval()
>>> print(dice)
0.20467791371802546
"""
def __init__(self, smooth=1e-5):
super(Dice, self).__init__()
self.smooth = validator.check_positive_float(smooth, "smooth")
self._dice_coeff_sum = 0
self._samples_num = 0
self.clear()
[docs] def clear(self):
"""Clears the internal evaluation result."""
self._dice_coeff_sum = 0
self._samples_num = 0
[docs] @rearrange_inputs
def update(self, *inputs):
"""
Updates the internal evaluation result :math:`y_pred` and :math:`y`.
Args:
inputs: Input `y_pred` and `y`. `y_pred` and `y` are Tensor, list or numpy.ndarray. `y_pred` is the
predicted value, `y` is the true value. The shape of `y_pred` and `y` are both :math:`(N, ...)`.
Raises:
ValueError: If the number of the inputs is not 2.
RuntimeError: If y_pred and y do not have the same shape.
"""
if len(inputs) != 2:
raise ValueError("For 'Dice.update', it needs 2 inputs (predicted value, true value), "
"but got {}.".format(len(inputs)))
y_pred = self._convert_data(inputs[0])
y = self._convert_data(inputs[1])
self._samples_num += y.shape[0]
if y_pred.shape != y.shape:
raise ValueError(f"For 'Dice.update', predicted value (input[0]) and true value (input[1]) "
f"should have same shape, but got predicted value shape: {y_pred.shape}, "
f"true value shape: {y.shape}.")
intersection = np.dot(y_pred.flatten(), y.flatten())
unionset = np.dot(y_pred.flatten(), y_pred.flatten()) + np.dot(y.flatten(), y.flatten())
single_dice_coeff = 2 * float(intersection) / float(unionset + self.smooth)
self._dice_coeff_sum += single_dice_coeff
[docs] def eval(self):
r"""
Computes the Dice.
Returns:
Float, the computed result.
Raises:
RuntimeError: If the total number of samples is 0.
"""
if self._samples_num == 0:
raise RuntimeError("The 'Dice coefficient' can not be calculated, because the number of samples is 0, "
"please check whether your inputs(predicted value, true value) are empty, or has "
"called update method before calling eval method.")
return self._dice_coeff_sum / float(self._samples_num)